The Quantitative Neuroscience Training Program (QNTP) within the PNI Ph.D. Program
About the QNTP
The QNTP is a program offered to students who are currently enrolled in the Neuroscience Ph.D. program at Princeton University. The PNI Ph.D. program is designed to provide all of our students with a strong foundation in quantitative approaches to neuroscience. The QNTP then builds on this foundation by giving our most quantitatively-focused predoctoral trainees the additional tools and training they need to function fully as computational neuroscientists.
The QNTP is supported by a T32 training grant from the NIMH that was renewed in 2013. All PNI graduate students who receive support from this T32 training grant are members of the QNTP and must complete the QNTP requirements listed below. In addition, PNI graduate students who are not receiving support from the training grant can opt to join the QNTP. All students who successfully complete the QNTP requirements listed below will receive a credential in “Quantitative and Computational Neuroscience” from the PNI upon receiving their Ph.D.
All QNTP trainees are required to complete the requirements of the PNI Ph.D. program (e.g., 501 and 502, attendance at the PNI retreat and neuro seminar). In addition to these core requirements, QNTP trainees also need to complete the following requirements:
Coursework. QNTP trainees are required to take two electives in total (in contrast to non-QNTP trainees, who only need to take one elective). These electives provide in-depth coverage of mathematical and computational methods for formal theory development and data analysis in neuroscience; see List of Electives below. At least one elective must be taken from a set of 12 Computational Neuroscience electives. The second elective can be drawn from the same set of Computational Neuroscience electives, or from a broader set of elective courses that provide advanced training in relevant quantitative methods.
Quantitative and Computational Neuroscience (QCN) Journal Club. To keep QNTP trainees informed about relevant developments in the field, all predoctoral members of the QNTP are required to attend a weekly QCN journal club and present once a year. The journal club organizers (trainees in the program) will choose a broad theme for each meeting, always with a quantitative focus, and then solicit volunteers to present a background and a focus paper on the subject. The journal club focuses on recent articles in the literature, but occasional informal presentations of recent findings from the trainee’s laboratory will also be encouraged. Note that the journal club is open to all Princeton trainees doing relevant research (not just PNI graduate students in the QNTP).
Thesis Committee. For students in the training program, at least one committee member should be actively conducting quantitative/computational research and one must be conducting experimental research.
Quantitatively-Focused Research Seminars. Students in the training program must attend at least one quantitatively-focused research seminar on a regular basis. These seminars include: PDP Meeting, the Neuroimaging Analysis Methods Meeting, the Quantitative and Computational Biology Seminar, the Biophysics Seminar, the Physical Biology Journal Club, and the Nonlinear Dynamical Systems Seminar. Students can petition the QNTP Executive Committee to have other research seminars count for this requirement if necessary.
Research Presentations. Students in the QNTP will be required to present their ongoing thesis research least once at each of the following venues: (1) the annual PNI retreat or the PNI in-house seminar; and (2) a quantitatively-oriented conference. Of these presentations, at least one is required to provide an informed consideration of the relevance of their work to clinical disorders.
In addition to the QNTP-specific activities listed above, QNTP trainees are required to participate in the following additional PNI-related activities:
Annual PNI Retreat. The retreat is a one day event that is held at the end of the spring semester, beginning with a brief introduction by the Directors of the PNI, in which they mention notable events and recent developments. The morning and afternoon are devoted to research presentations by graduate students and postdocs, followed by an invited lecture by a distinguished speaker. There is a poster session in the evening, followed by dinner, followed by community-building games.
Neuroscience Seminar. This is a series of presentations by distinguished outside visitors (plus occasional in-house faculty), attended by neuroscience graduate students, postdoctoral fellows, and faculty. Given the strong quantitative focus of our graduate program, many of these presentations focus on quantitative and computational neuroscience.
Princeton Neuroscience Institute In-House Seminar Series. This postdoc-run lunchtime seminar series is designed to train both grad students and postdocs in presentation of their research. Two trainees, always graduate students or postdocs, are each allotted 30 minutes to present their research.
Clinical Neuroscience Evening Seminar Series. PNI runs (under the aegis of the QNTP) a clinical neuroscience evening seminar series. To encourage participation, and interaction between the trainees and the speaker, it takes place over a pizza dinner, and only trainees are allowed to attend.
Computational Cognitive Neuropsychiatry (CCNP) Seminar Series. Students are encouraged to attend biweekly meetings of the Rutgers-Princeton CCNP; these meetings give trainees additional exposure to clinical issues and how they can be addressed via quantitative and computational methods. At these meetings, ongoing research projects are discussed and speakers (both internal and external) give presentations.
What does it mean to receive a “credential in Quantitative and Computational Neuroscience from the PNI”?
If you join the QNTP and complete the requirements, you will receive a certificate from the PNI attesting that you received additional training in Quantitative and Computational Neuroscience. You can list this on your CV. Note that the actual Ph.D. certificate issued by the graduate school will just say “Neuroscience”.
If I am a PNI graduate student who is not participating in the QNTP, can I take advantage of the training opportunities listed above (e.g., the QCN Journal Club)?
Yes, absolutely! All of the training opportunities listed above are open to all PNI graduate students.
LIST OF ELECTIVES (CURRENT AS OF SPRING 2017)
Computational neuroscience courses:
NEU 437/537 Computational Neuroscience (Brody)
NEU 330 Introduction to Connectionist Models: Bridging between Brain and Mind (Norman)
NEU/PSY 338 Animal Learning and Decision Making: Psychological, Computational and Neural Perspectives (Niv)
NEU/PSY 340 Neuroeconomics (Daw)
NEU 422 Dynamics in Cognition (Tank / Leifer)
NEU 457 Measurement and Analysis of Neural Circuit Dynamics (Leifer / Tank)
NEU 460/560 Advanced Statistical Methods for Neural Data (Pillow)
COS 495 Neural Networks: Theory and Applications (Seung)
MAT 323 Mathematical Neuroscience (Holmes)
PHY 555 Topics in Biophysics (Bialek)
PSY 503 Quantitative Methods in Psychology (Daw)
ELE/NEU/PSY 480 fMRI Decoding: Reading Minds Using Brain Scans (Norman, Ramadge)
Additional computational courses:
Machine Learning and Statistics
COS 324 Introduction to Machine Learning (Singer)
COS 424 Interacting with Data (Engelhardt)
COS 429 Advanced Topics in Computer Vision (Russakovsky)
COS 511, Theoretical Machine Learning (Hazan)
COS 513 Foundations of Probabilistic Modeling (Engelhardt)
COS 598E Unsupervised Learning: Theory and Practice (Hazan)
ORF 350 Analysis of Big Data (Liu)
ORF 525 Statistical Learning and Nonparametric Estimation (Liu)
ELE 535 Machine Learning and Pattern Recognition (Ramadge)
Engineering and Applied Math
ELE 521 Linear Systems Theory (Ramadge)
MAE 434 Modern Control (Leonard)
ELE 523 Nonlinear Systems Theory (Leonard)
MAE 541 Applied Dynamical Systems (Holmes)
MAE 542 Advanced Dynamics (Leonard)
APC 529 Coding Theory and Random Graphs (Abbe)
MAE 546 Optimal Control and Estimation (Stengel)
COS 521 Advanced Algorithm Design (Arora)
COS 551 Introduction to Computational Molecular Biology (Baryshnikova)
COS 598B Natural Algorithms (Chazelle)
APC 524 Software Engineering for Scientific Computing (Stone)
MOL 515 Methods and Logic in Quantitative Biology (Wingreen)
Students can petition the QNTP Executive Committee to have other courses (not listed above) count for this requirement if necessary.